{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "biblical-chrome",
   "metadata": {},
   "source": [
    "<div align=\"center\">\n",
    "<img src =\"https://annual.ametsoc.org/themes/annual/images/AMS22_Logo_300.png\" width=25% alt=\"American Meteorological Society logo\">\n",
    "<img src=\"../../logos/unidata_logo_horizontal.png\" width=25% alt=\"Unidata logo\">\n",
    "</div>\n",
    "\n",
    "# Python Workshop\n",
    "### AMS Student Conference 2022\n",
    "\n",
    "<div class=\"admonition alert alert-info\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Note</p>\n",
    "    If running this notebook on a local computer, the <code>pyaos-ams-2022</code> conda environment must be activated.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "native-librarian",
   "metadata": {},
   "source": [
    "## Table of Contents <a class=\"anchor\" id=\"top\"></a>\n",
    "\n",
    "* [Objective](#objective)\n",
    "* [Strategy](#strategy)\n",
    "* [Step 0: Import required packages](#step0)\n",
    "* [Step 1: Browse the THREDDS Data Server (TDS)](#step1)\n",
    "* [Step 2: Model output, equivalent potential temperature ($\\theta_e$)](#step2)\n",
    "    * [Step 2a: Obtain Model Output](#step2a)\n",
    "    * [Step 2b: Prepare Model Output](#step2b)\n",
    "    * [Step 2c: Visualize Model Output](#step2c)"
   ]
  },
  {
   "attachments": {
    "e141bb24-2e9b-415e-a737-cab827f14c63.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "corrected-injection",
   "metadata": {},
   "source": [
    "## Objective\n",
    "Create a color-filled contour plot of equivalent potential temperature ($\\theta_e$) on geographic axes.\n",
    "\n",
    "![AMS2022SC_final_output.png](attachment:e141bb24-2e9b-415e-a737-cab827f14c63.png)"
   ]
  },
  {
   "attachments": {
    "d2d922f4-2adc-4597-8a32-3de125a0efb0.png": {
     "image/png": 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j/Czwd4xZE2+ER/PkJMVI5VulHxQeJg37pSNR642/1ATrdFS0L3YU4ei4zThCi2VuOEttt7vJw52EicPHCgjNxESy8wOcQeEB3JUnvn6tusbQ4s3waJ6cRMBJQ8JjxxkKbdoWHeH2yvAop0uQDg8t+uiPy5VO0iz6yPBok35AeAAf6aG3ngeQLr3W+k4vzwO+LLTIr8qrxUq72wcLEY93krVMJyC+4PjBM+SNw6PalahPlSR3NOEwq/o7QzDk6OdBvsGcsoaK+1uBk4aERzhowm5/pzNmZhXhYdFhOwOf2oxe4bFHYfY27BKL8LAu2CHH20PDo+21kgPCQwkjpHc+mRDJcbT9i50Ynr8uG3mozThCq/FlB+g2wdiwKHKUsLPP3lc+H+8kGzNl4+JmnuycwsImCo9aVyLjuAZGh5m1XPApDLdWqUWHjdTGbFt1fyvo45DwsD5ioPJttHbu4Tk4GLhw2rzgkAsD2zqMdbfC48hS1nxcEkfFoQ/vfKHVca0C0dFXD0Yq3/HSVa29RdzDkI7ONCwPUwtPMDy38so7IzaO8CNs+tUN5P6whBfM2Xdx0neLxzsJhxXFUAzN9KOtLQbNBwaxtZ/hYZllV6I+RXlW1wkfOpKt4W8YtzoOGoPhJYIbmh82qiq4vxV0clB4WHXAOsVv4NcXtHCxBomTbIsrNhj87o4CvcXWKOvjTkFpv1hBaN+Lccq3Ct/XSX65hH3ERRiAwA7XS6wTSmCwF51fdxnHKNNswrGVH6ro4GIbGXiBNE9wkjWN34WmDARCjyltUaIEuhIZh8N2jBClMLZtf+Jgv1wSBp/VrYS/RDCYx+7vAr0cFh54xeYW8Wf1ng54Z79nox9+8RmXm7m2R3jwhZ0nKMyhFb1j046q3lF19YWcvRilfLvwI8OD3lm/+dXX6HeP4o2QHcYVjmYN/+xq79g8bVdbntyal6IXct7gCU7a6yQ6Do/12YTeafe/c3fYJFJ25XZ40DPh/D7ibF6y4QU1Ku/YtE2s9zg5rL6Qsw10c2B4zJ8xyncIP52TOChmwKydNBfQzxweJV3C5/CI+H4nzQV0NIdHQafwUzkJR0SGlp7JjJ00H9DTHB6BbuGzkwqW5qSsvEgIn50UWJyTsvJOSvjsJLE8Jy1G+f924KWSwmcneakFOmkxynfBQmnhs5NYaIlOysobN4TPTkKZRTppGcp34Y+PvSV8dpIVWaaTsvK3hc9OWqyTsvK3hc9OWqyTFq98D+GzkxbrpMUr38c+O2mpTsrK9zDPTlqqk9D1V0N69QDKK5nCq30t1LUeLNlJ+n4ppFcPTHmlkqjel0Jd60F20lL5tXQH9CE7aan80ncmQXZSZpH8aj7rMJO5gzySMplFYefcOegzE5BH0hLR9avMzJFc04LfMyYLejYzM3sk17TkkbRAslA/BMXVtDDkpwp6ryszdyTXtOSRtEBcKN1/lZkllEhxNS0K+WmCnhWpyZlZQokk17TkkbQsKFEWav5QIsXVtBQhP0XQsxo1OTNLKJHkmpY8kpYFJcpCzR9KpLiaFob8H/yNrDE26FmJmpyZJZRIck1LHknLghJloeYPJVJcTYuHvD+exxgX9KxCTc7MEkokuaYlj6RlQYmyUPOHEimupiWE/CRBzwrY4MxMoUSSa1rySFoWlCgLNX8okeJqWsqQnyDoWdzryswTSiS5piWPpGVBibJQ84cSKa7S0HIoCvnRQc/CqiozSyiR5EpDy6HkkbQYKFEWav5QIsVVGloOpQj5kUHPoqooM0sokeRKQ8uh5JG0GChRFmr+UCLFVRpa6g2Z/dFmyIigZ0FVk5kllEhypaGlxkd/tBmSR9IrQ4myUPOHEimu0tBSpe7l7qBnMVWSmSWUSHKloaVK3UseSa8LJcpCzR9KpLhKQ0uVup87g56FVEVmllAiyZWGlip1P3kkvSqU6MFCva/ISYujOKAmpcdwXLNNOy3OD0qkuEpDS5Uaw11BzyKqYPGcNxxUU4zPCaFEkisNLVVqDPMZSbvviPEgc8su7dSaOxl7bnV10OKjoUTfHfKXPUTb7s++mJw8rrbmTekePHjygFrqxYOhRIqrNLRUqXHcEfQsoOIToKHi7PYXZf8Qek4en2byofQDoESSKw0tVWocjxpJHx7Gq9X6aEsX7HbIu69e6OSxNX8oOSmU6Fsnj3PQE6y154WI7T6Fu59z/I956NaWu9v93VAixVUaWqrUWAYHPc1VeAIqkwcIe4GfAvceSneBgffAg0dKJLnS0FKlxvKYkXRr8vhGnrRrwGZvDR54QMlJoUTfOHlcoeb26mnsCtY8esyTx1AokeIqDS1VajwDg57GKjoNkcuPllz7WPop5Mmj4EEj6cO8WYYxhsxW6e8lTx5Tc0G7y4sNF0wlOPlAl/G/s/n01Yy0Cp7/piWwKa4aldal85RXHIC83drVfBannCukfNSdPcPZM8so2yt8s+3Gk0OJFFdpaKlSUzAo6GmqgtMQB2XYzdLXxtdVQ0Ojgocn4h3ShzFyKGTehdGInUyAx6lEGbdrTnGOhjY+PDc+A9+FKtCjCtrvtRpPAyWSXGloqVJT8JiRBKVCHCI4dbhRerq6iz9vlU0wuqRelAxFUqrE45S4mVE5Iq0LruzKjki7w24ORUPWSPmOKD2gC7yN7cYDoUTfN3nAo7ErEMw4EAj6WCR/Ml20Hxadx/+fELp6Cto4bGN1a9YMPZKOwXHJamcNPEvT2pYvzC4nv+YIiWgYTwolUlyloaVKTcOAoKehik1D7HLsF4LYcDZ4O191TZcXSNcfpsCZF4M3Ht5IIkIuX9cjRoSf+5acMU1stABob9ysuR0ab47Xr4t+SKsd9V2ZHY3LxhCOaBiPhxJJrjS0VKlpeMhIKsMeP6BWf6NsBDCM3z5NzxMPD7RziSVpifk2Vdp3DY3dGWp2M+58tD1u+812RNcj57LkT2DeUOvWSQc4tcHTOqCVbNAwHgAl+r7Jo9FsZcDT5ayCpTAlJCePlgobkReLaMnUL0VQodgW5+LGlmvVt4+QQGovMBZKpLhKQ0uVmoreQU8zFZqGyOUY54VClrYDkWjPAAHLi1o8IeQ0gUQhGs996/vimqos0KfmVirX1vy0VAsFtTGeHjY3AmI4lEhypaGlSk3FA0YSL5VTvubRYz2AKRAnD2NfnE7EkrTGfFOV9l1Dw86OR3g/UDx18IymHAEY5tUj5AroVGHM6aMxeFoGtJIttDe8D5To+yYPeKWIOqB+oMFRlwu1u2LlxPu1Vmt+Vnp6a/JIeA0DJ5pzUVPY8vXjDRqttviMmtPq6C7jSaFEiqs0tFSp6egZ9DRSkWmg5IFww4WBRSUdGlShGEiUZ78QXQcUl8MOA2/Fz2gMIU9JBxk1umVGZeXUguFcVHZ852Hlhp/KM9onjw7j8VAiyZWGlio1Hd8/kqix7XZwcFi7kNwIYNzqFtgUE03n5NGtSuuuoWvqr0wd3FqdZlUCRzLR1ILNhpb2HtCgy3gAlOj7Jg+4JZ5EcdIPOdHl6HJW5PgWb9PTERW/NiIvFhHWSrZQ2xRq4iIuVsRENs0RkjCeFEqkuEpDS5WakH4xTxuVmAa4nGzeK55HlpIOjapQDCTKkrySYd+VmwCNVKzRoEq3zFirpFFOHrzYEMFM0hjCKePxUCLJlYaWKjUh3z+SsMfYlgEd0Qxgmz/2vhsF+iuBWJKySFKVtpo7Jg/WE+0WsbU6zapEbbRgs1wcMqBTxgOgRN83edBPutnKQE/pSiTKC8eutdP0Nqoofvi2dNWvjciLRYS1km1gv18cbPBkF1vmj/yhhtoAbIyQlPGkUCLFVRpaqtRk9L3aQCuVmYaGywUcr6SDQ7I278Ow+J3jqpN+DLlNyMQmUrHWVXMrOD4qdwyompVpu6ScUUhjCKeMx0OJJFcaWqrUZDxkJGGfASc2LubXR9Pnuvy5mGPDV8aSFEXSqrSP05bJI5o63rRvxK6nbYy3waOf4tSW7UCjBg3olPEAKNE3Th7lz4f6tdKvO6DBNmFY9teZtw6UDoYV/hrs8kkvn+jsnRW7HJlRc/OYyYO26wPrxma9GZgP1h/WspPu0YnEp9dxynT6wLpd0nhSKJHiKg0tVWoi+gb8UycPvwKxOWKEnU8f79vVmie3yDWRiiHIU94g5PVz7wdhqVjrqrkdjMgVzpKufi+IV4YtY1ifP6JMhwcgDIUj1yWNx0OJJFcaWqrURDxqJOl6QNhBFtRHE4zWe+Rc6Hz9WsWwtlzfB6lIWpV6zU5j8mCFmq+w0scdm/t+so1fT5+4npTYjfAod3NEQ3TXFepIDeiwB72euO6QNB4AJfrOycNaKAGMN4qpBd/ZksqFyXCPirF+R4HyLzjXOAYE1lkJGGEO50RMFMQgJYT+RtPYhWYWf7hpRJnONdrslmMgYTwllEhxlYaWKjUJ/QN+8o3LoU6hY0P6ELTRPbLAf/RGal8MIQQRiITca09j9Q+ruYviTvHVJgzCUzSso0xRjiFbid1XyngslEhypaGlSk3CA0cSHRj5jD6swB1mtBMywuAwQiyvo5miXRWlIlBzsfcpYb1KF4Tddnn3LVgn7/kv//gxGrvtA9op96C2D4ZTUsb9oUTfO3lkpoASKa7S0FKlJmBIwH/D/mY8iIzJ9rwvACWSXGloqVIT8ONHUqYKJcpCzR9KpLhKQ0uVGs2wgM+Txw+AEkmuNLRUqdG8wEjKVKFEWaj5Q4kUV2loqVIjGRrwswv56AS/ctffkqFEkisNLVVqJD9+JGWaUKIs1PyhRIqrNLRUqVEMD/g8efwAKJHkSkNLlRrFC4ykTBNKlIWaP5RIcZWGlio1gnsCPof8D4ASSa40tFSpEeSR9KJQoizU/KFEiqs0tFSpu7kv4HPI/wAokeRKQ0uVups8kl4WSpSFmj+USHGVhpYqdSf3BnwO+R8AJZJcaWipUneSR9ILQ4myUPOHEimu0tByKH+0GXB/wOeQ/wFQIsmVhpZDySNpMVCiLNT8oUSKqzS0HEoZ8mMCPof8D4ASSa40tBxKHkmLgRJloeYPJVJcpfl7IP+PmkPIjwv4HPI/AEokudJogPQmj6RlQYmyUPOHEimupuUXavaQHxvwOeR/AJRIck1LHknLghJloeYPJVJcTUsR8uMDPof8D4ASSa5pySNpWVCiLNT8oUSKq2lRyE8R8DnkfwCUSHJNSx5Jy4ISZaHmDyVSXE0LQ/4/rH9swOeQ/wFQIsk1LXkkLQtKlIWaP5RIcTUtDHkyOuBzyP8AKJHkmpY8kpYFJXKhMrNHcTUtIeQnCHiFfGb2SK5pySNpgWShfgiKq2nxkJ8k4HPI/xQk17TkkbRAslA/BMXVtCDkJwr4HPI/Bck1LXkkLRDJlVkkv6YL+MyiySMpk1kU/9J3JjOOPJIWx19//Q/nCyonNhhaVgAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "id": "b10a5da2-465d-4ad9-a90e-551d7dd7db28",
   "metadata": {},
   "source": [
    "[Top](#top)\n",
    "\n",
    "## Strategy <a class=\"anchor\" id=\"strategy\"></a>\n",
    "\n",
    "Creating plots like this using MetPy follows this basic workflow:\n",
    "\n",
    "![level1_2.png](attachment:d2d922f4-2adc-4597-8a32-3de125a0efb0.png)\n",
    "\n",
    "We will use the Real-time Meso-analysis (RTMA) output available on the Unidata-hosted THREDDS Data Server, then calculate $\\theta_e$ from the available variables, and finally plot the field on geographic axes. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "advisory-tenant",
   "metadata": {},
   "source": [
    "[Top](#top)\n",
    "\n",
    "## Step 0: Import required packages <a class=\"anchor\" id=\"step0\"></a>\n",
    "\n",
    "First, we need to import all our required packages. Today we're working with:\n",
    "- cartopy\n",
    "- matplotlib\n",
    "- metpy\n",
    "- siphon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "numeric-organic",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "\n",
    "# We will use the standard \"plt\" abbreviation to access matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import metpy.calc as mpcalc\n",
    "import metpy.plots as mpplots\n",
    "\n",
    "# Here is where we import the TDSCatalog class from siphon for obtaining our data \n",
    "from siphon.catalog import TDSCatalog"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "addressed-drinking",
   "metadata": {},
   "source": [
    "[Top](#top)\n",
    "\n",
    "## Step 1: Browse the THREDDS Data Server (TDS) <a class=\"anchor\" id=\"step1\"></a>\n",
    "\n",
    "The **THREDDS Data Server** provides us with coherent access to a large collection of real-time and archived datasets from a variety of environmental data sources at a number of distributed server sites. \n",
    "You can browse the TDS in your web browser using this link: <a href=\"https://thredds.ucar.edu/\" target =\"blank\">https://thredds.ucar.edu/</a>\n"
   ]
  },
  {
   "attachments": {
    "rtma_workflow.png": {
     "image/png": 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    }
   },
   "cell_type": "markdown",
   "id": "direct-article",
   "metadata": {
    "tags": []
   },
   "source": [
    "[Top](#top)\n",
    "\n",
    "## Step 2: Obtain, Prepare, and Visualize an Equivalent Potential Temperature ($\\theta_e$) Field <a class=\"anchor\" id=\"step2\"></a>\n",
    "\n",
    "Now we grab data from the [Real-time Meso-analysis (RTMA)](https://nomads.ncep.noaa.gov/txt_descriptions/RTMA_doc.shtml), which gives us a gridded estimate of the realtime conditions. Our goal is to use MetPy to calculate <a href=\"https://glossary.ametsoc.org/wiki/Equivalent_potential_temperature\" target=\"blank\">equivalent potential temperature ($\\theta_e$)</a>, using the fields we have available in the RTMA. \n",
    "\n",
    "We are using the basic obtain > prepare > visualize workflow to accomplish this task. We can expand our strategy diagram to include more detail as below: \n",
    "\n",
    "<div>\n",
    "<img src=\"attachment:rtma_workflow.png\" width=\"800\">\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "portuguese-boost",
   "metadata": {
    "tags": []
   },
   "source": [
    "[Top](#top)\n",
    "\n",
    "### Step 2a: Obtain Model Output <a class=\"anchor\" id=\"step2a\"></a>\n",
    "\n",
    "We will use siphon's TDSCatalog object to create a catalog of satellite data from the TDS that match our criteria (RTMA from NCEP, over CONUS). Then we use siphon's remote_access method to read in the latest dataset from the catalog into an `xarray.Dataset`.\n",
    "\n",
    "Notice the use of the `squeeze()` method! Our provided dataset might have stray dimensions that we don't really benefit from keeping around. Feel free to compare what this dataset looks like with and without using this.\n",
    "\n",
    "<div class=\"admonition alert alert-info\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Note</p>\n",
    "    Notice the URL in the TDSCatalog input ends with .xml rather than .html. While your web browser uses html to show you the data in a human-friendly webpage, siphon requires an xml document to create the TDSCatalog object.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "weighted-parks",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create TDS catalog object\n",
    "rtma_cat = TDSCatalog('https://thredds.ucar.edu/thredds/catalog/grib/NCEP/RTMA/CONUS_2p5km/catalog.xml')\n",
    "\n",
    "# Using xarray, we pull the data from the TDS into an xarray DataSet object\n",
    "rtma_data = rtma_cat.datasets['Latest Collection for Real Time Mesoscale Analysis 2.5 km'].remote_access(use_xarray=True)\n",
    "rtma_data = rtma_data.metpy.parse_cf().squeeze()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "further-tobago",
   "metadata": {
    "tags": []
   },
   "source": [
    "[Top](#top)\n",
    "\n",
    "### Step 2b: Prepare Model Output and Calculate $\\theta_e$ <a class=\"anchor\" id=\"step2b\"></a>\n",
    "\n",
    "Before we calculate a derived variable such as $\\theta_e$, we need to look up the required variables to calculate that variable. Looking at the metpy.calc documentation for <a href =\"https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.equivalent_potential_temperature.html#metpy.calc.equivalent_potential_temperature\" target=\"blank\">equivalent potential temperature</a>, we can see that this function requires three variables: **surface pressure**, **temperature**, and **dewpoint**. Each of these variables are contained in the rtma_data Dataset, but we need to know the name of each variable as it is stored in the Dataset. \n",
    "\n",
    "\n",
    "<div class=\"admonition alert alert-warning\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Activity: Find RTMA Variable Names</p>\n",
    "    <code>rtma_data</code> is an xarray Dataset containing several variables. Use the cell below to programmatically display the list of all variables in the Dataset, then find the names of the <b>surface pressure</b>, <b>temperature</b>, and <b>dewpoint</b> variables. \n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "compatible-utility",
   "metadata": {},
   "outputs": [
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       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                                                              (: 2, x: 2145, y: 1377)\n",
       "Coordinates:\n",
       "    reftime                                                              datetime64[ns] ...\n",
       "    time1                                                                datetime64[ns] ...\n",
       "  * x                                                                    (x) float32 ...\n",
       "  * y                                                                    (y) float32 ...\n",
       "    time                                                                 datetime64[ns] ...\n",
       "    height_above_ground                                                  float32 ...\n",
       "    metpy_crs                                                            object ...\n",
       "    altitude_above_msl                                                   float32 ...\n",
       "    height_above_ground1                                                 float32 ...\n",
       "Dimensions without coordinates: \n",
       "Data variables: (12/22)\n",
       "    LambertConformal_Projection                                          &gt;i4 0\n",
       "    time1_bounds                                                         () datetime64[ns] ...\n",
       "    Dewpoint_temperature_error_height_above_ground                       (y, x) float32 ...\n",
       "    Dewpoint_temperature_Analysis_height_above_ground                    (y, x) float32 ...\n",
       "    Geopotential_height_Analysis_surface                                 (y, x) float32 ...\n",
       "    Pressure_error_surface                                               (y, x) float32 ...\n",
       "    ...                                                                   ...\n",
       "    Wind_speed_error_height_above_ground                                 (y, x) float32 ...\n",
       "    Wind_speed_Analysis_height_above_ground                              (y, x) float32 ...\n",
       "    Wind_speed_gust_error_height_above_ground                            (y, x) float32 ...\n",
       "    Wind_speed_gust_Analysis_height_above_ground                         (y, x) float32 ...\n",
       "    u-component_of_wind_Analysis_height_above_ground                     (y, x) float32 ...\n",
       "    v-component_of_wind_Analysis_height_above_ground                     (y, x) float32 ...\n",
       "Attributes:\n",
       "    Originating_or_generating_Center:                                        ...\n",
       "    Originating_or_generating_Subcenter:                                     ...\n",
       "    GRIB_table_version:                                                      ...\n",
       "    Type_of_generating_process:                                              ...\n",
       "    Analysis_or_forecast_generating_process_identifier_defined_by_originating...\n",
       "    file_format:                                                             ...\n",
       "    Conventions:                                                             ...\n",
       "    history:                                                                 ...\n",
       "    featureType:                                                             ...\n",
       "    _CoordSysBuilder:                                                        ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-65727c76-96c0-4596-acfd-4a6deceeeea3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-65727c76-96c0-4596-acfd-4a6deceeeea3' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span></span>: 2</li><li><span class='xr-has-index'>x</span>: 2145</li><li><span class='xr-has-index'>y</span>: 1377</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-baeab09e-ccec-4f78-b31a-edef07a93ac6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-baeab09e-ccec-4f78-b31a-edef07a93ac6' class='xr-section-summary' >Coordinates: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>reftime</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-20T23:00:00</div><input id='attrs-c7047092-f8cb-4077-b8e0-0779340bb9d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7047092-f8cb-4077-b8e0-0779340bb9d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2fc1e8d0-fb77-445e-b804-3ffea0fb2419' class='xr-var-data-in' type='checkbox'><label for='data-2fc1e8d0-fb77-445e-b804-3ffea0fb2419' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd><dt><span>long_name :</span></dt><dd>GRIB reference time</dd><dt><span>_CoordinateAxisType :</span></dt><dd>RunTime</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2022-01-20T23:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time1</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-21</div><input id='attrs-4bbea239-994f-4f91-82a4-583516092728' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4bbea239-994f-4f91-82a4-583516092728' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26b39415-b8aa-4aff-972a-9108a3496667' class='xr-var-data-in' type='checkbox'><label for='data-26b39415-b8aa-4aff-972a-9108a3496667' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>GRIB forecast or observation time</dd><dt><span>bounds :</span></dt><dd>time1_bounds</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd><dt><span>_metpy_axis :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2022-01-21T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-2.763e+06 -2.761e+06 ... 2.682e+06</div><input id='attrs-14eddf13-e0b7-4d9c-a6fb-a992833df115' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-14eddf13-e0b7-4d9c-a6fb-a992833df115' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90a80c99-d7fa-450e-8213-227ec3ea8dac' class='xr-var-data-in' type='checkbox'><label for='data-90a80c99-d7fa-450e-8213-227ec3ea8dac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>_CoordinateAxisType :</span></dt><dd>GeoX</dd></dl></div><div class='xr-var-data'><pre>array([-2763204.5, -2760664.8, -2758125. , ...,  2676839. ,  2679379. ,\n",
       "        2681918.5], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-2.638e+05 -2.612e+05 ... 3.231e+06</div><input id='attrs-af912996-8a1e-4d84-baea-43a91d981b43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af912996-8a1e-4d84-baea-43a91d981b43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e54bd94-adf0-486d-9221-8edac48e92b9' class='xr-var-data-in' type='checkbox'><label for='data-5e54bd94-adf0-486d-9221-8edac48e92b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>_CoordinateAxisType :</span></dt><dd>GeoY</dd></dl></div><div class='xr-var-data'><pre>array([-263789.44, -261249.72, -258710.02, ..., 3225762.5 , 3228302.  ,\n",
       "       3230841.8 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-20T23:00:00</div><input id='attrs-09568437-6230-43e1-80a2-41d710be9002' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-09568437-6230-43e1-80a2-41d710be9002' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95808991-acad-4d02-8719-09f58cd4539f' class='xr-var-data-in' type='checkbox'><label for='data-95808991-acad-4d02-8719-09f58cd4539f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>GRIB forecast or observation time</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2022-01-20T23:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height_above_ground</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-74e03a6b-d94d-485e-a950-dfa81c391420' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74e03a6b-d94d-485e-a950-dfa81c391420' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c814372c-c7d4-4a0c-8098-e833cbfe87b4' class='xr-var-data-in' type='checkbox'><label for='data-c814372c-c7d4-4a0c-8098-e833cbfe87b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Specified height level above ground</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>Grib_level_type :</span></dt><dd>103</dd><dt><span>datum :</span></dt><dd>ground</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Height</dd><dt><span>_CoordinateZisPositive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>array(2., dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>metpy_crs</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>Projection: lambert_conformal_conic</div><input id='attrs-1e52ce02-35a4-488b-9666-d693704c4198' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e52ce02-35a4-488b-9666-d693704c4198' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe1a321a-9345-44e0-af8e-b9cd8484e844' class='xr-var-data-in' type='checkbox'><label for='data-fe1a321a-9345-44e0-af8e-b9cd8484e844' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&lt;metpy.plots.mapping.CFProjection object at 0x15fc41a80&gt;,\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>altitude_above_msl</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-701c33f0-7b92-4ff3-abf8-af2590998c8b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-701c33f0-7b92-4ff3-abf8-af2590998c8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00c43b66-9d95-447e-9904-c299fcf503f3' class='xr-var-data-in' type='checkbox'><label for='data-00c43b66-9d95-447e-9904-c299fcf503f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Specific altitude above mean sea level</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>Grib_level_type :</span></dt><dd>102</dd><dt><span>datum :</span></dt><dd>mean sea level</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Height</dd><dt><span>_CoordinateZisPositive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>array(0., dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height_above_ground1</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>10.0</div><input id='attrs-40a7797f-0aec-4d73-b87c-0b8d8a5a9f82' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40a7797f-0aec-4d73-b87c-0b8d8a5a9f82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4aceee07-e836-49c6-b9b9-c1536e94dd3c' class='xr-var-data-in' type='checkbox'><label for='data-4aceee07-e836-49c6-b9b9-c1536e94dd3c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Specified height level above ground</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>Grib_level_type :</span></dt><dd>103</dd><dt><span>datum :</span></dt><dd>ground</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Height</dd><dt><span>_CoordinateZisPositive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>array(10., dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9949f8a0-be21-4eaa-9411-92b8c63049d2' class='xr-section-summary-in' type='checkbox'  ><label for='section-9949f8a0-be21-4eaa-9411-92b8c63049d2' class='xr-section-summary' >Data variables: <span>(22)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>LambertConformal_Projection</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-40b11e71-2a4a-4eb4-b9b5-bfd2b07da53c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40b11e71-2a4a-4eb4-b9b5-bfd2b07da53c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1acd6daf-19ac-491d-a2fb-c4047507d81e' class='xr-var-data-in' type='checkbox'><label for='data-1acd6daf-19ac-491d-a2fb-c4047507d81e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping_name :</span></dt><dd>lambert_conformal_conic</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>25.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>265.0</dd><dt><span>standard_parallel :</span></dt><dd>25.0</dd><dt><span>earth_radius :</span></dt><dd>6371200.0</dd><dt><span>_CoordinateTransformType :</span></dt><dd>Projection</dd><dt><span>_CoordinateAxisTypes :</span></dt><dd>GeoX GeoY</dd></dl></div><div class='xr-var-data'><pre>array(0, dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time1_bounds</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-606ab9ee-b263-43e8-a9a6-dce044988736' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-606ab9ee-b263-43e8-a9a6-dce044988736' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32beab6e-5c35-4f54-93a0-3b10256b6af5' class='xr-var-data-in' type='checkbox'><label for='data-32beab6e-5c35-4f54-93a0-3b10256b6af5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>bounds for time1</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-20T23:00:00.000000000&#x27;, &#x27;2022-01-21T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Dewpoint_temperature_error_height_above_ground</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-af6d8f8c-68a0-4d9d-b43b-89d323e52c96' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af6d8f8c-68a0-4d9d-b43b-89d323e52c96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fae77879-7325-4c72-8850-ab75b9ed3b87' class='xr-var-data-in' type='checkbox'><label for='data-fae77879-7325-4c72-8850-ab75b9ed3b87' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Dewpoint temperature error @ Specified height level above ground</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>abbreviation :</span></dt><dd>DPT</dd><dt><span>grid_mapping :</span></dt><dd>LambertConformal_Projection</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_0-0-6_error_L103</dd><dt><span>Grib2_Parameter :</span></dt><dd>[0 0 6]</dd><dt><span>Grib2_Parameter_Discipline :</span></dt><dd>Meteorological products</dd><dt><span>Grib2_Parameter_Category :</span></dt><dd>Temperature</dd><dt><span>Grib2_Parameter_Name :</span></dt><dd>Dewpoint temperature</dd><dt><span>Grib2_Level_Type :</span></dt><dd>103</dd><dt><span>Grib2_Level_Desc :</span></dt><dd>Specified height level above ground</dd><dt><span>Grib2_Generating_Process_Type :</span></dt><dd>Analysis error</dd></dl></div><div class='xr-var-data'><pre>[2953665 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Dewpoint_temperature_Analysis_height_above_ground</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e72aac39-504c-4e12-990a-98d811123290' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e72aac39-504c-4e12-990a-98d811123290' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cccf828-859d-41b7-a97a-fc723ce917e2' class='xr-var-data-in' type='checkbox'><label for='data-8cccf828-859d-41b7-a97a-fc723ce917e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Dewpoint temperature Analysis @ Specified height level above ground</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>abbreviation :</span></dt><dd>DPT</dd><dt><span>grid_mapping :</span></dt><dd>LambertConformal_Projection</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_0-0-6_L103</dd><dt><span>Grib2_Parameter :</span></dt><dd>[0 0 6]</dd><dt><span>Grib2_Parameter_Discipline :</span></dt><dd>Meteorological products</dd><dt><span>Grib2_Parameter_Category :</span></dt><dd>Temperature</dd><dt><span>Grib2_Parameter_Name :</span></dt><dd>Dewpoint temperature</dd><dt><span>Grib2_Level_Type :</span></dt><dd>103</dd><dt><span>Grib2_Level_Desc :</span></dt><dd>Specified height level above ground</dd><dt><span>Grib2_Generating_Process_Type :</span></dt><dd>Analysis</dd></dl></div><div class='xr-var-data'><pre>[2953665 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Geopotential_height_Analysis_surface</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0912e3e3-0ef9-4f18-9070-74b55eaca8e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0912e3e3-0ef9-4f18-9070-74b55eaca8e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fab43571-47a0-4739-b8d6-138b337e2249' class='xr-var-data-in' type='checkbox'><label for='data-fab43571-47a0-4739-b8d6-138b337e2249' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Geopotential height Analysis @ Ground or water surface</dd><dt><span>units :</span></dt><dd>gpm</dd><dt><span>abbreviation :</span></dt><dd>HGT</dd><dt><span>grid_mapping :</span></dt><dd>LambertConformal_Projection</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_0-3-5_L1</dd><dt><span>Grib2_Parameter :</span></dt><dd>[0 3 5]</dd><dt><span>Grib2_Parameter_Discipline :</span></dt><dd>Meteorological products</dd><dt><span>Grib2_Parameter_Category :</span></dt><dd>Mass</dd><dt><span>Grib2_Parameter_Name :</span></dt><dd>Geopotential height</dd><dt><span>Grib2_Level_Type :</span></dt><dd>1</dd><dt><span>Grib2_Level_Desc :</span></dt><dd>Ground or water surface</dd><dt><span>Grib2_Generating_Process_Type :</span></dt><dd>Analysis</dd></dl></div><div class='xr-var-data'><pre>[2953665 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Pressure_error_surface</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1ee5ee1b-1950-4fe1-a65c-7cbdcb437a80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ee5ee1b-1950-4fe1-a65c-7cbdcb437a80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-941b3719-8da6-42f8-b96c-fd53176a9f7e' class='xr-var-data-in' type='checkbox'><label for='data-941b3719-8da6-42f8-b96c-fd53176a9f7e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Pressure error @ Ground or water surface</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>abbreviation :</span></dt><dd>PRES</dd><dt><span>grid_mapping :</span></dt><dd>LambertConformal_Projection</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_0-3-0_error_L1</dd><dt><span>Grib2_Parameter :</span></dt><dd>[0 3 0]</dd><dt><span>Grib2_Parameter_Discipline :</span></dt><dd>Meteorological products</dd><dt><span>Grib2_Parameter_Category :</span></dt><dd>Mass</dd><dt><span>Grib2_Parameter_Name :</span></dt><dd>Pressure</dd><dt><span>Grib2_Level_Type :</span></dt><dd>1</dd><dt><span>Grib2_Level_Desc :</span></dt><dd>Ground or water surface</dd><dt><span>Grib2_Generating_Process_Type :</span></dt><dd>Analysis error</dd></dl></div><div class='xr-var-data'><pre>[2953665 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Pressure_Analysis_surface</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5646d595-a79e-44b1-bc2a-5256241c3984' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5646d595-a79e-44b1-bc2a-5256241c3984' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5fc0a390-4c3e-408f-a4b0-34aae9cc7f00' class='xr-var-data-in' type='checkbox'><label for='data-5fc0a390-4c3e-408f-a4b0-34aae9cc7f00' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Pressure Analysis @ Ground or water surface</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>abbreviation :</span></dt><dd>PRES</dd><dt><span>grid_mapping :</span></dt><dd>LambertConformal_Projection</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_0-3-0_L1</dd><dt><span>Grib2_Parameter :</span></dt><dd>[0 3 0]</dd><dt><span>Grib2_Parameter_Discipline :</span></dt><dd>Meteorological products</dd><dt><span>Grib2_Parameter_Category :</span></dt><dd>Mass</dd><dt><span>Grib2_Parameter_Name :</span></dt><dd>Pressure</dd><dt><span>Grib2_Level_Type :</span></dt><dd>1</dd><dt><span>Grib2_Level_Desc :</span></dt><dd>Ground or water surface</dd><dt><span>Grib2_Generating_Process_Type :</span></dt><dd>Analysis</dd></dl></div><div class='xr-var-data'><pre>[2953665 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Temperature_error_height_above_ground</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bf5d2d07-a37f-4fde-8ec3-f5533ed8c5f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf5d2d07-a37f-4fde-8ec3-f5533ed8c5f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a673f649-9771-4a69-bdc0-9b1650bfca1d' class='xr-var-data-in' type='checkbox'><label for='data-a673f649-9771-4a69-bdc0-9b1650bfca1d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature error @ Specified height level above ground</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>abbreviation :</span></dt><dd>TMP</dd><dt><span>grid_mapping :</span></dt><dd>LambertConformal_Projection</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_0-0-0_error_L103</dd><dt><span>Grib2_Parameter :</span></dt><dd>[0 0 0]</dd><dt><span>Grib2_Parameter_Discipline :</span></dt><dd>Meteorological products</dd><dt><span>Grib2_Parameter_Category :</span></dt><dd>Temperature</dd><dt><span>Grib2_Parameter_Name :</span></dt><dd>Temperature</dd><dt><span>Grib2_Level_Type :</span></dt><dd>103</dd><dt><span>Grib2_Level_Desc :</span></dt><dd>Specified height level above ground</dd><dt><span>Grib2_Generating_Process_Type :</span></dt><dd>Analysis error</dd></dl></div><div class='xr-var-data'><pre>[2953665 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Temperature_Analysis_height_above_ground</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-29fffed6-b4d8-4726-9c20-9f7e2d47b4eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29fffed6-b4d8-4726-9c20-9f7e2d47b4eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24e7129f-7071-4bd7-99ea-191141f0eb04' class='xr-var-data-in' type='checkbox'><label for='data-24e7129f-7071-4bd7-99ea-191141f0eb04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature Analysis @ Specified height level above ground</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>abbreviation :</span></dt><dd>TMP</dd><dt><span>grid_mapping :</span></dt><dd>LambertConformal_Projection</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_0-0-0_L103</dd><dt><span>Grib2_Parameter :</span></dt><dd>[0 0 0]</dd><dt><span>Grib2_Parameter_Discipline :</span></dt><dd>Meteorological products</dd><dt><span>Grib2_Parameter_Category :</span></dt><dd>Temperature</dd><dt><span>Grib2_Parameter_Name :</span></dt><dd>Temperature</dd><dt><span>Grib2_Level_Type :</span></dt><dd>103</dd><dt><span>Grib2_Level_Desc :</span></dt><dd>Specified height level above ground</dd><dt><span>Grib2_Generating_Process_Type :</span></dt><dd>Analysis</dd></dl></div><div class='xr-var-data'><pre>[2953665 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Total_cloud_cover_Analysis_entire_atmosphere_single_layer</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c99026d2-dd91-4022-b1eb-7f85cef8cc52' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c99026d2-dd91-4022-b1eb-7f85cef8cc52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c6e99f9-17cd-4cc3-ac3d-4c2d075ed20c' class='xr-var-data-in' type='checkbox'><label for='data-8c6e99f9-17cd-4cc3-ac3d-4c2d075ed20c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total cloud cover Analysis @ Entire atmosphere layer</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>abbreviation :</span></dt><dd>TCDC</dd><dt><span>grid_mapping :</span></dt><dd>LambertConformal_Projection</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_0-6-1_L200</dd><dt><span>Grib2_Parameter :</span></dt><dd>[0 6 1]</dd><dt><span>Grib2_Parameter_Discipline :</span></dt><dd>Meteorological products</dd><dt><span>Grib2_Parameter_Category :</span></dt><dd>Cloud</dd><dt><span>Grib2_Parameter_Name :</span></dt><dd>Total cloud cover</dd><dt><span>Grib2_Level_Type :</span></dt><dd>200</dd><dt><span>Grib2_Level_Desc :</span></dt><dd>Entire atmosphere layer</dd><dt><span>Grib2_Generating_Process_Type :</span></dt><dd>Analysis</dd></dl></div><div class='xr-var-data'><pre>[2953665 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Total_cloud_cover_error_entire_atmosphere_single_layer</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-edb4fa24-80f4-4717-97fa-5de95eec67cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-edb4fa24-80f4-4717-97fa-5de95eec67cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7a97ded-2677-4eaa-903f-ac04a82b4fc8' class='xr-var-data-in' type='checkbox'><label for='data-c7a97ded-2677-4eaa-903f-ac04a82b4fc8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total cloud cover error @ Entire atmosphere layer</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>abbreviation :</span></dt><dd>TCDC</dd><dt><span>grid_mapping :</span></dt><dd>LambertConformal_Projection</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_0-6-1_error_L200</dd><dt><span>Grib2_Parameter :</span></dt><dd>[0 6 1]</dd><dt><span>Grib2_Parameter_Discipline :</span></dt><dd>Meteorological products</dd><dt><span>Grib2_Parameter_Category :</span></dt><dd>Cloud</dd><dt><span>Grib2_Parameter_Name :</span></dt><dd>Total cloud cover</dd><dt><span>Grib2_Level_Type :</span></dt><dd>200</dd><dt><span>Grib2_Level_Desc :</span></dt><dd>Entire atmosphere layer</dd><dt><span>Grib2_Generating_Process_Type :</span></dt><dd>Analysis error</dd></dl></div><div class='xr-var-data'><pre>[2953665 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Total_precipitation_Forecast_altitude_above_msl_1_Hour_Accumulation</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5207c88a-a362-4e70-a4fc-bb2ce73d40c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5207c88a-a362-4e70-a4fc-bb2ce73d40c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab4f6a81-2e16-4632-8a0f-d5a22107710e' class='xr-var-data-in' type='checkbox'><label for='data-ab4f6a81-2e16-4632-8a0f-d5a22107710e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total precipitation (1_Hour Accumulation) Forecast @ Specific altitude above mean sea level</dd><dt><span>units :</span></dt><dd>kg.m-2</dd><dt><span>abbreviation :</span></dt><dd>APCP</dd><dt><span>grid_mapping :</span></dt><dd>LambertConformal_Projection</dd><dt><span>Grib_Statistical_Interval_Type :</span></dt><dd>Accumulation</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_0-1-8_L102_I1_Hour_S1</dd><dt><span>Grib2_Parameter :</span></dt><dd>[0 1 8]</dd><dt><span>Grib2_Parameter_Discipline :</span></dt><dd>Meteorological products</dd><dt><span>Grib2_Parameter_Category :</span></dt><dd>Moisture</dd><dt><span>Grib2_Parameter_Name :</span></dt><dd>Total precipitation</dd><dt><span>Grib2_Level_Type :</span></dt><dd>102</dd><dt><span>Grib2_Level_Desc :</span></dt><dd>Specific altitude above mean sea level</dd><dt><span>Grib2_Generating_Process_Type :</span></dt><dd>Forecast</dd></dl></div><div class='xr-var-data'><pre>[2953665 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Visibility_Analysis_surface</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9f3e039e-94fb-44e5-9e2c-34a8b84b3bb8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f3e039e-94fb-44e5-9e2c-34a8b84b3bb8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce9b61ce-b2e3-4e7a-ba6b-26a85ab635a2' class='xr-var-data-in' type='checkbox'><label for='data-ce9b61ce-b2e3-4e7a-ba6b-26a85ab635a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Visibility Analysis @ Ground or water surface</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>abbreviation :</span></dt><dd>VIS</dd><dt><span>grid_mapping :</span></dt><dd>LambertConformal_Projection</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_0-19-0_L1</dd><dt><span>Grib2_Parameter :</span></dt><dd>[ 0 19  0]</dd><dt><span>Grib2_Parameter_Discipline :</span></dt><dd>Meteorological products</dd><dt><span>Grib2_Parameter_Category :</span></dt><dd>Physical atmospheric Properties</dd><dt><span>Grib2_Parameter_Name :</span></dt><dd>Visibility</dd><dt><span>Grib2_Level_Type :</span></dt><dd>1</dd><dt><span>Grib2_Level_Desc :</span></dt><dd>Ground or water surface</dd><dt><span>Grib2_Generating_Process_Type :</span></dt><dd>Analysis</dd></dl></div><div class='xr-var-data'><pre>[2953665 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Visibility_error_surface</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7f67272d-a0ca-4812-8c37-8d241fc1309a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f67272d-a0ca-4812-8c37-8d241fc1309a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f1b0740-49ab-4470-b343-2c72d1686588' class='xr-var-data-in' type='checkbox'><label for='data-0f1b0740-49ab-4470-b343-2c72d1686588' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Visibility error @ Ground or water surface</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>abbreviation :</span></dt><dd>VIS</dd><dt><span>grid_mapping :</span></dt><dd>LambertConformal_Projection</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_0-19-0_error_L1</dd><dt><span>Grib2_Parameter :</span></dt><dd>[ 0 19  0]</dd><dt><span>Grib2_Parameter_Discipline :</span></dt><dd>Meteorological products</dd><dt><span>Grib2_Parameter_Category :</span></dt><dd>Physical atmospheric Properties</dd><dt><span>Grib2_Parameter_Name :</span></dt><dd>Visibility</dd><dt><span>Grib2_Level_Type :</span></dt><dd>1</dd><dt><span>Grib2_Level_Desc :</span></dt><dd>Ground or water surface</dd><dt><span>Grib2_Generating_Process_Type :</span></dt><dd>Analysis error</dd></dl></div><div class='xr-var-data'><pre>[2953665 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Wind_direction_from_which_blowing_error_height_above_ground</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-62246807-dc3c-4325-b34b-b3b9f9dd527d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-62246807-dc3c-4325-b34b-b3b9f9dd527d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93d5d43b-29c7-42f6-883e-6b8c6093dd45' class='xr-var-data-in' type='checkbox'><label for='data-93d5d43b-29c7-42f6-883e-6b8c6093dd45' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Wind direction (from which blowing) error @ Specified height level above ground</dd><dt><span>units :</span></dt><dd>degree_true</dd><dt><span>abbreviation :</span></dt><dd>WDIR</dd><dt><span>grid_mapping :</span></dt><dd>LambertConformal_Projection</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_0-2-0_error_L103</dd><dt><span>Grib2_Parameter :</span></dt><dd>[0 2 0]</dd><dt><span>Grib2_Parameter_Discipline :</span></dt><dd>Meteorological products</dd><dt><span>Grib2_Parameter_Category :</span></dt><dd>Momentum</dd><dt><span>Grib2_Parameter_Name :</span></dt><dd>Wind direction (from which blowing)</dd><dt><span>Grib2_Level_Type :</span></dt><dd>103</dd><dt><span>Grib2_Level_Desc :</span></dt><dd>Specified height level above ground</dd><dt><span>Grib2_Generating_Process_Type :</span></dt><dd>Analysis error</dd></dl></div><div class='xr-var-data'><pre>[2953665 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Wind_direction_from_which_blowing_Analysis_height_above_ground</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-86700e9e-a90b-4445-b279-3a1a0b546dac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86700e9e-a90b-4445-b279-3a1a0b546dac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-536ed146-d9b3-4878-a3b0-5a27ba234560' class='xr-var-data-in' type='checkbox'><label for='data-536ed146-d9b3-4878-a3b0-5a27ba234560' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Wind direction (from which blowing) Analysis @ Specified height level above ground</dd><dt><span>units :</span></dt><dd>degree_true</dd><dt><span>abbreviation :</span></dt><dd>WDIR</dd><dt><span>grid_mapping :</span></dt><dd>LambertConformal_Projection</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_0-2-0_L103</dd><dt><span>Grib2_Parameter :</span></dt><dd>[0 2 0]</dd><dt><span>Grib2_Parameter_Discipline :</span></dt><dd>Meteorological products</dd><dt><span>Grib2_Parameter_Category :</span></dt><dd>Momentum</dd><dt><span>Grib2_Parameter_Name :</span></dt><dd>Wind direction (from which blowing)</dd><dt><span>Grib2_Level_Type :</span></dt><dd>103</dd><dt><span>Grib2_Level_Desc :</span></dt><dd>Specified height level above ground</dd><dt><span>Grib2_Generating_Process_Type :</span></dt><dd>Analysis</dd></dl></div><div class='xr-var-data'><pre>[2953665 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Wind_speed_error_height_above_ground</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a805a2d8-6e36-4ce5-be81-41b4dcc12ab0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a805a2d8-6e36-4ce5-be81-41b4dcc12ab0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f83e1845-2d00-44fa-bc74-7add87083d36' class='xr-var-data-in' type='checkbox'><label for='data-f83e1845-2d00-44fa-bc74-7add87083d36' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Wind speed error @ Specified height level above ground</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>abbreviation :</span></dt><dd>WIND</dd><dt><span>grid_mapping :</span></dt><dd>LambertConformal_Projection</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_0-2-1_error_L103</dd><dt><span>Grib2_Parameter :</span></dt><dd>[0 2 1]</dd><dt><span>Grib2_Parameter_Discipline :</span></dt><dd>Meteorological products</dd><dt><span>Grib2_Parameter_Category :</span></dt><dd>Momentum</dd><dt><span>Grib2_Parameter_Name :</span></dt><dd>Wind speed</dd><dt><span>Grib2_Level_Type :</span></dt><dd>103</dd><dt><span>Grib2_Level_Desc :</span></dt><dd>Specified height level above ground</dd><dt><span>Grib2_Generating_Process_Type :</span></dt><dd>Analysis error</dd></dl></div><div class='xr-var-data'><pre>[2953665 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Wind_speed_Analysis_height_above_ground</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-43f566e3-ac3f-473f-aa6c-d4e6d1d4816d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-43f566e3-ac3f-473f-aa6c-d4e6d1d4816d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3fae3fa-03b5-41dd-9722-d425cf9cebd6' class='xr-var-data-in' type='checkbox'><label for='data-f3fae3fa-03b5-41dd-9722-d425cf9cebd6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Wind speed Analysis @ Specified height level above ground</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>abbreviation :</span></dt><dd>WIND</dd><dt><span>grid_mapping :</span></dt><dd>LambertConformal_Projection</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_0-2-1_L103</dd><dt><span>Grib2_Parameter :</span></dt><dd>[0 2 1]</dd><dt><span>Grib2_Parameter_Discipline :</span></dt><dd>Meteorological products</dd><dt><span>Grib2_Parameter_Category :</span></dt><dd>Momentum</dd><dt><span>Grib2_Parameter_Name :</span></dt><dd>Wind speed</dd><dt><span>Grib2_Level_Type :</span></dt><dd>103</dd><dt><span>Grib2_Level_Desc :</span></dt><dd>Specified height level above ground</dd><dt><span>Grib2_Generating_Process_Type :</span></dt><dd>Analysis</dd></dl></div><div class='xr-var-data'><pre>[2953665 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Wind_speed_gust_error_height_above_ground</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-625c946d-47fa-4b9e-b988-6b86a5ae706b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-625c946d-47fa-4b9e-b988-6b86a5ae706b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ea60d9a-5c1b-43e6-ac1a-9ce2383489f2' class='xr-var-data-in' type='checkbox'><label for='data-2ea60d9a-5c1b-43e6-ac1a-9ce2383489f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Wind speed (gust) error @ Specified height level above ground</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>abbreviation :</span></dt><dd>GUST</dd><dt><span>grid_mapping :</span></dt><dd>LambertConformal_Projection</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_0-2-22_error_L103</dd><dt><span>Grib2_Parameter :</span></dt><dd>[ 0  2 22]</dd><dt><span>Grib2_Parameter_Discipline :</span></dt><dd>Meteorological products</dd><dt><span>Grib2_Parameter_Category :</span></dt><dd>Momentum</dd><dt><span>Grib2_Parameter_Name :</span></dt><dd>Wind speed (gust)</dd><dt><span>Grib2_Level_Type :</span></dt><dd>103</dd><dt><span>Grib2_Level_Desc :</span></dt><dd>Specified height level above ground</dd><dt><span>Grib2_Generating_Process_Type :</span></dt><dd>Analysis error</dd></dl></div><div class='xr-var-data'><pre>[2953665 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Wind_speed_gust_Analysis_height_above_ground</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0e824dcd-0703-426d-aee4-3979eae62301' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e824dcd-0703-426d-aee4-3979eae62301' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e081a0b2-d6dd-4f1c-8eb2-6843d11c68f2' class='xr-var-data-in' type='checkbox'><label for='data-e081a0b2-d6dd-4f1c-8eb2-6843d11c68f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Wind speed (gust) Analysis @ Specified height level above ground</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>abbreviation :</span></dt><dd>GUST</dd><dt><span>grid_mapping :</span></dt><dd>LambertConformal_Projection</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_0-2-22_L103</dd><dt><span>Grib2_Parameter :</span></dt><dd>[ 0  2 22]</dd><dt><span>Grib2_Parameter_Discipline :</span></dt><dd>Meteorological products</dd><dt><span>Grib2_Parameter_Category :</span></dt><dd>Momentum</dd><dt><span>Grib2_Parameter_Name :</span></dt><dd>Wind speed (gust)</dd><dt><span>Grib2_Level_Type :</span></dt><dd>103</dd><dt><span>Grib2_Level_Desc :</span></dt><dd>Specified height level above ground</dd><dt><span>Grib2_Generating_Process_Type :</span></dt><dd>Analysis</dd></dl></div><div class='xr-var-data'><pre>[2953665 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u-component_of_wind_Analysis_height_above_ground</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-13aa06e1-2a8a-4ab4-b860-9f64c134199b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13aa06e1-2a8a-4ab4-b860-9f64c134199b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94d68077-c097-423a-a658-327167e87511' class='xr-var-data-in' type='checkbox'><label for='data-94d68077-c097-423a-a658-327167e87511' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>u-component of wind Analysis @ Specified height level above ground</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>abbreviation :</span></dt><dd>UGRD</dd><dt><span>grid_mapping :</span></dt><dd>LambertConformal_Projection</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_0-2-2_L103</dd><dt><span>Grib2_Parameter :</span></dt><dd>[0 2 2]</dd><dt><span>Grib2_Parameter_Discipline :</span></dt><dd>Meteorological products</dd><dt><span>Grib2_Parameter_Category :</span></dt><dd>Momentum</dd><dt><span>Grib2_Parameter_Name :</span></dt><dd>u-component of wind</dd><dt><span>Grib2_Level_Type :</span></dt><dd>103</dd><dt><span>Grib2_Level_Desc :</span></dt><dd>Specified height level above ground</dd><dt><span>Grib2_Generating_Process_Type :</span></dt><dd>Analysis</dd></dl></div><div class='xr-var-data'><pre>[2953665 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v-component_of_wind_Analysis_height_above_ground</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6ed221a3-bd0e-4b42-8c60-ae494cf557a5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ed221a3-bd0e-4b42-8c60-ae494cf557a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d277c3e9-2b49-43be-8ac8-95cf24b6ca01' class='xr-var-data-in' type='checkbox'><label for='data-d277c3e9-2b49-43be-8ac8-95cf24b6ca01' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>v-component of wind Analysis @ Specified height level above ground</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>abbreviation :</span></dt><dd>VGRD</dd><dt><span>grid_mapping :</span></dt><dd>LambertConformal_Projection</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_0-2-3_L103</dd><dt><span>Grib2_Parameter :</span></dt><dd>[0 2 3]</dd><dt><span>Grib2_Parameter_Discipline :</span></dt><dd>Meteorological products</dd><dt><span>Grib2_Parameter_Category :</span></dt><dd>Momentum</dd><dt><span>Grib2_Parameter_Name :</span></dt><dd>v-component of wind</dd><dt><span>Grib2_Level_Type :</span></dt><dd>103</dd><dt><span>Grib2_Level_Desc :</span></dt><dd>Specified height level above ground</dd><dt><span>Grib2_Generating_Process_Type :</span></dt><dd>Analysis</dd></dl></div><div class='xr-var-data'><pre>[2953665 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2824a754-0544-4ea9-8fd4-cf3535634875' class='xr-section-summary-in' type='checkbox'  ><label for='section-2824a754-0544-4ea9-8fd4-cf3535634875' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Originating_or_generating_Center :</span></dt><dd>US National Weather Service, National Centres for Environmental Prediction (NCEP)</dd><dt><span>Originating_or_generating_Subcenter :</span></dt><dd>Environmental Modeling Center</dd><dt><span>GRIB_table_version :</span></dt><dd>1,1</dd><dt><span>Type_of_generating_process :</span></dt><dd>Analysis error</dd><dt><span>Analysis_or_forecast_generating_process_identifier_defined_by_originating_centre :</span></dt><dd>RTMA (Real Time Mesoscale Analysis)</dd><dt><span>file_format :</span></dt><dd>GRIB-2</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>history :</span></dt><dd>Read using CDM IOSP GribCollection v3</dd><dt><span>featureType :</span></dt><dd>GRID</dd><dt><span>_CoordSysBuilder :</span></dt><dd>ucar.nc2.dataset.conv.CF1Convention</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                                                              (: 2, x: 2145, y: 1377)\n",
       "Coordinates:\n",
       "    reftime                                                              datetime64[ns] ...\n",
       "    time1                                                                datetime64[ns] ...\n",
       "  * x                                                                    (x) float32 ...\n",
       "  * y                                                                    (y) float32 ...\n",
       "    time                                                                 datetime64[ns] ...\n",
       "    height_above_ground                                                  float32 ...\n",
       "    metpy_crs                                                            object ...\n",
       "    altitude_above_msl                                                   float32 ...\n",
       "    height_above_ground1                                                 float32 ...\n",
       "Dimensions without coordinates: \n",
       "Data variables: (12/22)\n",
       "    LambertConformal_Projection                                          int32 ...\n",
       "    time1_bounds                                                         () datetime64[ns] ...\n",
       "    Dewpoint_temperature_error_height_above_ground                       (y, x) float32 ...\n",
       "    Dewpoint_temperature_Analysis_height_above_ground                    (y, x) float32 ...\n",
       "    Geopotential_height_Analysis_surface                                 (y, x) float32 ...\n",
       "    Pressure_error_surface                                               (y, x) float32 ...\n",
       "    ...                                                                   ...\n",
       "    Wind_speed_error_height_above_ground                                 (y, x) float32 ...\n",
       "    Wind_speed_Analysis_height_above_ground                              (y, x) float32 ...\n",
       "    Wind_speed_gust_error_height_above_ground                            (y, x) float32 ...\n",
       "    Wind_speed_gust_Analysis_height_above_ground                         (y, x) float32 ...\n",
       "    u-component_of_wind_Analysis_height_above_ground                     (y, x) float32 ...\n",
       "    v-component_of_wind_Analysis_height_above_ground                     (y, x) float32 ...\n",
       "Attributes:\n",
       "    Originating_or_generating_Center:                                        ...\n",
       "    Originating_or_generating_Subcenter:                                     ...\n",
       "    GRIB_table_version:                                                      ...\n",
       "    Type_of_generating_process:                                              ...\n",
       "    Analysis_or_forecast_generating_process_identifier_defined_by_originating...\n",
       "    file_format:                                                             ...\n",
       "    Conventions:                                                             ...\n",
       "    history:                                                                 ...\n",
       "    featureType:                                                             ...\n",
       "    _CoordSysBuilder:                                                        ..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ACTIVITY: Find RTMA Variable Names\n",
    "\n",
    "## INSTRUCTOR'S ANSWER KEY ##\n",
    "# Recall from prework\n",
    "# Have learner share in chat window\n",
    "# NOTE: without using print, errors come up accessing one specific variable before the variable list is displayed\n",
    "rtma_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "closing-induction",
   "metadata": {},
   "source": [
    "Surface pressure is stored in the *\"Pressure_Analysis_surface\"* variable name, which we'll use to select the corresponding DataArray within. Note that creating a new variable is not *necessary*, but can save you some time later and make your code more readable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "muslim-nomination",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store the surface pressure DataArray to variable `pres`\n",
    "pres = rtma_data.Pressure_Analysis_surface"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcf81c4c-bb53-4d96-8dd9-b89b0d17607e",
   "metadata": {},
   "source": [
    "<div class=\"admonition alert alert-warning\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Activity: Create Temperature and Dewpoint DataArrays</p>\n",
    "    Using the information from the previous activity and following the demonstration in the previous cell, create <code>temp</code> and <code>dewp</code> variables that represent the surface temperature and dewpoint, respectively. \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "instrumental-monthly",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ACTIVITY: Create Temperature and Dewpoint DataArrays\n",
    "\n",
    "# temp = \n",
    "# dewp = \n",
    "\n",
    "\n",
    "## INSTRUCTOR'S ANSWER KEY ##\n",
    "temp = rtma_data.Temperature_Analysis_height_above_ground\n",
    "dewp = rtma_data.Dewpoint_temperature_Analysis_height_above_ground"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "brave-calculation",
   "metadata": {},
   "source": [
    "<div class=\"admonition alert alert-warning\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Activity: Calculate theta_e</p>\n",
    "    Now, calculate equivalent potential temperature using the  <a href =\"https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.html\" target=\"blank\">metpy.calc documentation</a>. Create a variable called <code>theta_e</code> to represent the equivalent potential temperature field.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "veterinary-dance",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ACTIVITY: Calculate theta_e\n",
    "\n",
    "\n",
    "# theta_e = \n",
    "\n",
    "\n",
    "\n",
    "## INSTRUCTOR'S ANSWER KEY ##\n",
    "theta_e = mpcalc.equivalent_potential_temperature(pres, temp, dewp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54be2ee2-73b7-476e-a408-e27f2d082563",
   "metadata": {},
   "source": [
    "<div class=\"admonition alert alert-warning\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Challenge: Calculate another variable</p>\n",
    "    Locate another derived variable in the metpy.calc documentation that takes in any combination of surface pressure, temperature, or dewpoint, then calculate it as a new variable. Optionally, plot it using any plotting tool to visualize the output.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "center-jones",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x15fd44af0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CHALLENGE: Calculate another variable\n",
    "\n",
    "\n",
    "\n",
    "## INSTRUCTOR'S ANSWER KEY ##\n",
    "# one of many possibilities\n",
    "rh = mpcalc.relative_humidity_from_dewpoint(temp, dewp)\n",
    "plt.imshow(rh, origin=\"lower\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b002f402-e106-452b-805f-3ac3d5c0d313",
   "metadata": {},
   "source": [
    "Now, smooth the theta_e array using a gaussian filter. This will improve the readability of the final plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "quiet-biotechnology",
   "metadata": {},
   "outputs": [],
   "source": [
    "# smooth the theta_e array\n",
    "theta_e = mpcalc.smooth_gaussian(theta_e, n=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "infrared-setting",
   "metadata": {},
   "source": [
    "We can use MetPy shortcuts to create a cartopy <a href=\"https://scitools.org.uk/cartopy/docs/latest/crs/index.html#cartopy.crs.CRS\">cartographic reference system</a> for our data. RTMA uses a Lambert Conformal projection, which we will use in our final plot. This is our final data preparation step for the `theta_e` DataArray. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "minute-resort",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the crs object for the theta_e array\n",
    "rtma_crs = theta_e.metpy.cartopy_crs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prostate-inspection",
   "metadata": {},
   "source": [
    "[Top](#top)\n",
    "\n",
    "### Step 2c: Visualize Model Output <a class=\"anchor\" id=\"step2c\"></a>\n",
    "\n",
    "The data are now prepared for plotting. Let's use **filled contours** for visualizing the $\\theta_e$ field and add cartopy's built-in `coastlines` class for geographic context.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "finite-hydrogen",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.feature_artist.FeatureArtist at 0x15fe04460>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create figure, axes, then use contourf to create filled contours of the theta_e field\n",
    "fig = plt.figure(figsize=[10,20])\n",
    "ax = fig.add_subplot(projection=rtma_crs)\n",
    "ax.contourf(theta_e['x'], theta_e['y'], theta_e, transform=rtma_crs)\n",
    "ax.coastlines()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "jewish-wales",
   "metadata": {},
   "source": [
    "<div class=\"admonition alert alert-info\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Tip</p>\n",
    "    If you are working with xarray DataArrays parsed via <code>metpy.parse_cf</code>, and you aren't sure what your horizontal variables (e.g. x, or lon, or latitude) are named, or you just want a shortcut, you can access these quickly with <code>xarray.metpy.x</code> and <code>xarray.metpy.y</code>.<br><br>For example, these two lines of code are equivalent for DataArrays parsed with <code>metpy.parse.cf</code>:<br><br>\n",
    "    <code>ax.contourf(theta_e['x'], theta_e['y'], theta_e, transform=rtma_crs)</code><br>\n",
    "    <code>ax.contourf(theta_e.metpy.x, theta_e.metpy.y, theta_e, transform=rtma_crs)</code>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92b09ec8-2621-4b31-926d-1fbfd61a662e",
   "metadata": {},
   "source": [
    "<div class=\"admonition alert alert-warning\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Activity: Contour plots</p>\n",
    "    You will complete this activity as a part of a team. You will be assigned a group number and you will document your output on your group's designated slide in the provided presentation.<br>\n",
    "    <ol>\n",
    "    <li>Plot <code>theta_e</code> using the <a href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.contour.html\" target=\"blank\">matplotlib <code>contour()</code> plot type</a>.</li>\n",
    "    <li>Use the LambertConformal projection in your axes, the same projection the RTMA dataset uses.</li>\n",
    "    <li>Add coastlines.</li>\n",
    "    <li>Make the contours a single color (<a href=\"https://matplotlib.org/stable/gallery/images_contours_and_fields/contour_demo.html\" target=\"blank\">see examples here</a>).</li>\n",
    "    <li>Add a descriptive title to your plot.</li>\n",
    "    <li>Copy your plot into your group's designated slide at the link provided.</li> \n",
    "        <dl>\n",
    "            <dd>To copy an image from a notebook: Shift + right click on the image, then select Copy image</dd>\n",
    "        </dl>\n",
    "    </ol>\n",
    "    <b>After your group has created the <code>theta_e</code> plot</b>, look to create more contour plots of other RTMA variables you have created in this notebook or any other <a href=\"https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.html\">calculations</a>. Choose any colors, but label your plots with titles that describe the variable(s) plotted. \n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "freelance-connectivity",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ACTIVITY: Contour plots\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "## INSTRUCTOR'S ANSWER KEY ##\n",
    "#ax = plt.axes(projection=rtma_crs)\n",
    "#ax.contour(theta_e.metpy.x, theta_e.metpy.y, theta_e, transform=rtma_crs, colors='blue')\n",
    "#ax.coastlines()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "544dff6e-ee37-44ab-bf00-8b17dbd3d114",
   "metadata": {},
   "source": [
    "<div class=\"admonition alert alert-warning\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Challenge: Make the plot your own</p>\n",
    "   Use the remaining time to stylize your plot to you liking. Use the cell below to experiment with your plot.<br> \n",
    "Some ideas:\n",
    "    <ul>\n",
    "        <li>Stylize the title of the plot, changing the size</li>\n",
    "        <li>Change the colors of the contours</li>\n",
    "        <li>Swap the theta_e contours with another RTMA variable, or add other contours</li>\n",
    "    </ul>\n",
    "Challenges: \n",
    "    <ul>\n",
    "        <li>Make this a multi-panel plot with separate axes for each variable plotted</li>\n",
    "        <li>Add labels to your theta_e contours</li>\n",
    "        <li>Review the MetPy example gallery and add elements to your plot</li>\n",
    "        <dl>\n",
    "            <dd><a href=\"https://unidata.github.io/MetPy/latest/examples/index.html\" target=\"blank\">https://unidata.github.io/MetPy/latest/examples/index.html</a></dd>\n",
    "        </dl>\n",
    "    </ul>\n",
    "Share your final plots with us! <a href=\"https://twitter.com/Metpy\" target=\"blank\">@metpy on Twitter</a> \n",
    "\n",
    "</div>"
   ]
  }
 ],
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